As background, in the remote sensing/aerial imaging industry, imagery is used to capture views of a geographic area and to be able to measure objects and structures within the images as well as to be able to determine geographic locations of points within the image. These are generally referred to as “geo-referenced images” and come in two basic categories:
Captured Imagery—these images have the appearance they were captured by the camera or sensor employed.
Projected Imagery—these images have been processed and converted such that they confirm to a mathematical projection.
All imagery starts as captured imagery, but as most software cannot geo-reference captured imagery, that imagery is then reprocessed to create the projected imagery. The most common form of projected imagery is the ortho-rectified image. This process aligns the image to an orthogonal or rectilinear grid (composed of rectangles). The input image used to create an ortho-rectified image is a nadir image—that is, an image captured with the camera pointing straight down. It is often quite desirable to combine multiple images into a larger composite image such that the image covers a larger geographic area on the ground. The most common form of this composite image is the “ortho-mosaic image” which is an image created from a series of overlapping or adjacent nadir images that are mathematically combined into a single ortho-rectified image.
When creating an ortho-mosaic, this same ortho-rectification process is used, however, instead of using only a single input nadir image, a collection of overlapping or adjacent nadir images are used and they are combined to form a single composite ortho-rectified image known as an ortho-mosaic. In general, the ortho-mosaic process entails the following steps:
A rectilinear grid is created, which results in an ortho-mosaic image where every grid pixel covers the same amount of area on the ground.
The location of each grid pixel is determined from the mathematical definition of the grid. Generally, this means the grid is given an X and Y starting or origin location and an X and Y size for the grid pixels. Thus, the location of any pixel is simply the origin location plus the number of pixels times the size of each pixel. In mathematical terms: Xpixel=Xorigin+Xsize×Columnpixel and Ypixel=Yorigin+Ysize×Rowpixel.
The available nadir images are checked to see if they cover the same point on the ground as the grid pixel being filled. If so, a mathematical formula is used to determine where that point on the ground projects up onto the camera's pixel image map and that resulting pixel value is then transferred to the grid pixel.
Because the rectilinear grids used for the ortho-mosaic are generally the same grids used for creating maps, the ortho-mosaic images bear a striking similarity to maps and as such, are generally very easy to use from a direction and orientation standpoint.
In producing the geo-referenced aerial images, hardware and software systems designed for georeferencing airborne sensor data exist. For example, a method and apparatus for mapping and measuring land is described in U.S. Pat. No. 5,247,356. In addition, a system produced by Applanix Corporation of Richmond Hill, Ontario, Canada and sold under the trademark “POS AV” provides a hardware and software system for directly georeferencing sensor data. Direct Georeferencing is the direct measurement of sensor position and orientation (also known as the exterior orientation parameters), without the need for additional ground information over the project area. These parameters allow data from the airborne sensor to be georeferenced to the Earth or local mapping frame. Examples of airborne sensors include: aerial cameras (digital or film-based), multi-spectral or hyper-spectral scanners, SAR, or LIDAR.
The POS AV system was mounted on a moving platform, such as an airplane, such that the airborne sensor was pointed toward the Earth. The positioning system received position signals from a satellite constellation and also received time signals from an accurate clock. The sensor was controlled by a computer running flight management software to take images. Signals indicative of the taking of an image were sent from the sensor to the positioning system to record the time and position where the image was taken.
When capturing images with a digital sensor, a variety of abnormalities such as elevated sensor noise levels, streaks, blooms or smears can be formed within the captured image. Such abnormalities can be caused by malfunctions of the image capture device, or by the external environment. For example, in aerial photography, in particular, reflections of the sun off of shiny or reflective surfaces such as lakes, windows, greenhouses or windshields can cause blooms which smear to form streaks in the captured image. An exemplary photograph having a streak formed from reflections off of water is shown in FIG. 15. When a streak is captured in an image, the image capture device's sensor is usually overstimulated near the location of the streak or hot spot. This typically ruins a part of the image and causes the manual rescheduling at a later time/date of another image of the same area to be taken. Because the abnormality is not detected until after the airplane has landed and the images are processed, the re-taking of another image of the same area typically results in time delays and costly re-flights.
Therefore, there is a need to eliminate the time delays and costly re-flights associated with abnormalities occurring in captured aerial imagery. It is to such a system for eliminating the time delays and costly re-flights that the present invention is directed.